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CAREER: Quantum Embedding of Wave-Function Methods as Path to High-Accuracy Thermochemistry in Heterogeneous Catalysis

职业：波函数方法的量子嵌入作为多相催化中高精度热化学的途径

基本信息

批准号：
1945276
负责人：
Gerald Knizia
金额：
$ 57.69万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945276&HistoricalAwards=false
关键词：
CAREER Quantum Embedding Wave Function

项目摘要

Gerald Knizia of Pennsylvania State University is supported by an award from the Chemical Theory, Models and Computational Methods program to develop an accurate and efficient theoretical approach to heterogenous catalysis. The focus is on reactions that take place on a hard surface, such as a metal, which acts as a catalyst. Catalysts are used in the production of household products and almost all industrial chemicals as they help speed up the chemical synthesis. Almost 25% of the global industrial-sector energy consumption is due to the catalytic production of basic chemicals and fuels alone! A sound microscopic understanding of the processes of chemical reactions at the smallest scale, obtained by computational analysis, could help substantially in developing new catalysts or improving existing ones. However, a key challenge is that the present computational methods applicable to surface reactions are not accurate enough to reliably identify which of the many competing reaction pathways are actually taking place in reality. This research addresses this challenge by enabling the use of high-accuracy computational methods from small-molecule theoretical chemistry, for use in the complex environments of realistic catalysis at surfaces. The research activities are integrated with an educational approach aimed at senior undergraduate and incoming graduate students. The goal of the educational activities is to help these students become proficient users of computational techniques. The broader technical impacts of the research may result in improvement in industrial processes (e.g., reducing waste, increasing energy efficiency, reducing dependence on imported precious metals, etc.), and thereby contribute to the economy of independence of the US. The educational materials will be made available to everyone, and may provide disadvantaged but bright students with poor access to educational resources with a starting point for learning powerful computing techniques. Concretely, the research targets the development of wave-function based electronic structure methods which have sufficient accuracy (~1 kcal/mol in relative energies) to allow for definitive thermochemical calculations on the surfaces of hard materials, such as current industrial heterogeneous catalysts. First, a suitable quantum embedding framework, based on the Density Matrix Embedding Theory (DMET) is developed. The method is specialized for embedding a single target fragment. This allows the polarization functions needed by thermochemical wave function methods and facilitates to be introduced using fast Kohn-Sham DFT for the mean-field description of the environment. Second, high-accuracy local coupled cluster methods will be developed which are capable of being used in the presence of the embedding. Third, the techniques from the first two steps will be adjusted to incorporate a coupling to an actual periodic surface system via imposing fixed boundary condition from ideal bulk/surface systems (as opposed to imposing periodic boundary conditions on the target system itself).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

宾夕法尼亚州立大学的Gerald Knizia获得了化学理论，模型和计算方法计划的奖项，以开发一种准确有效的多相催化理论方法。重点是在硬表面上发生的反应，如金属，它作为催化剂。催化剂用于生产家用产品和几乎所有的工业化学品，因为它们有助于加速化学合成。全球工业部门能源消耗的近25%来自基础化学品和燃料的催化生产！通过计算分析获得的对最小尺度化学反应过程的合理微观理解，可以大大有助于开发新的催化剂或改进现有的催化剂。然而，一个关键的挑战是，目前适用于表面反应的计算方法不够准确，无法可靠地识别许多竞争反应途径中的哪一个实际上正在发生。这项研究通过使用小分子理论化学的高精度计算方法来解决这一挑战，用于表面实际催化的复杂环境。研究活动与针对高年级本科生和即将入学的研究生的教育方法相结合。教育活动的目标是帮助这些学生成为熟练的计算技术的用户。研究的更广泛的技术影响可能会导致工业流程的改进（例如，减少浪费、提高能源效率、减少对进口贵金属的依赖等），从而为美国的经济独立做出贡献。教育材料将提供给每个人，并可能为弱势但聪明的学生提供一个学习强大的计算技术的起点。具体地说，该研究的目标是发展基于波函数的电子结构方法，这些方法具有足够的精度（相对能量约为1千卡/摩尔），以允许对硬质材料表面进行明确的热化学计算，例如当前的工业非均相催化剂。首先，一个合适的量子嵌入框架，基于密度矩阵嵌入理论（DMET）的发展。该方法专门用于嵌入单个目标片段。这使得所需的极化函数的热化学波函数方法，并有利于使用快速科恩-沙姆DFT的平均场描述的环境。第二，高精度的本地耦合集群的方法将被开发，这是能够被用于在嵌入的存在。第三，前两个步骤中的技术将被调整，通过从理想的体/表面系统施加固定边界条件（而不是在目标系统本身上施加周期性边界条件），将耦合纳入实际的周期性表面系统。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。